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Iterative Semi-Global Matching for Robust Driver Assistance Systems

  • Simon Hermann
  • Reinhard Klette
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7726)

Abstract

Semi-global matching (SGM) is a technique of choice for dense stereo estimation in current industrial driver-assistance systems due to its real-time processing capability and its convincing performance. In this paper we introduce iSGM as a new cost integration concept for semi-global matching. In iSGM, accumulated costs are iteratively evaluated and intermediate disparity results serve as input to generate semi-global distance maps. This novel data structure supports fast analysis of spatial disparity information and allows for reliable search space reduction in consecutive cost accumulation. As a consequence horizontal costs are stabilized which improves the robustness of the matching result. We demonstrate the superiority of this iterative integration concept against a standard configuration of semi-global matching and compare our results to current state-of-the-art methods on the KITTI Vision Benchmark Suite.

Keywords

Search Space Consistency Check Stereo Match Stereo Pair Search Space Reduction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Simon Hermann
    • 1
  • Reinhard Klette
    • 1
  1. 1.The .enpeda.. Project, Department of Computer ScienceThe University of AucklandNew Zealand

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